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Publié le 6 avril 2017

StatLearn 2017
The workshop Statlearn is a premier event held every year, which focuses on current and upcoming trends in Statistical Learning.
Statlearn’17, the 8th edition of the workshop, would be held in Lyon on April, 6-7 2017.
Statlearn’17 was a conference of the French Society of Statistics (SFdS).

Statistical advances in machine learning (chair: C. Friguet)

Diffusion phenomena in networks : virality, influence and control - Nicolas Vayatis (Ecole Normale Supérieure de Cachan)

Autoregressive Generative Models with Deep Learning - Hugo Larochelle (Google)
On MCMC methods for tall data - Rémi Bardenet (CNRS – Université de Lille)

High-dimensional & big data (chair: C. Bouveyron)

Scalable Programming Strategies for Massive Data in R - John W. Emerson (Yale University, web)

An Adaptive Ridge Procedure for L0 Regularization and Applications - Grégory Nuel (CNRS – Université Pierre et Marie Curie, web)
Real time community detection in large networks - Sébastien Loustau (artfact)

Text mining (chair: P. Latouche)

From Word Vectors to Sentence Representations - Martin Jaggi (Ecole Polytechnique Fédérale de Lausanne)

Novelties and limits of neural approaches for information access - Benjamin Piwowarski (CNRS – Université Pierre et Marie Curie)
NLP-driven Data Journalism: Event-based Extraction and Aggregation of International Alliances Relations - Xavier Tannier (Université Paris Sud)

New and future problems in statistical learning (chair: J. Jacques)

About two disinherited sides of statistics: data units and computational saving - Christophe Biernacki (Université de Lille)
ABC random forests for Bayesian parameter inference - Christian Robert (Université Paris Dauphine)

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